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emotion_detection.train()

Fine-tunes a pre-trained Emotion Detection model on user-specified classes and domain. No training data is needed, as the model uses synthex.jobs.generate_data() under the hood to generate a synthetic training dataset based on the provided classes and domain.

Both the base Emotion Detection model tanaos/tanaos-emotion-detection-v1 and previously trained models can be further trained using this method:

  • Fine-tune the base Emotion Detection model: 
    Artifex().emotion_detection.train()
  • Fine-tune a model that was previously trained with Artifex, in order to train it further: 
    Artifex().emotion_detection.load("trained/model/path").train()

Arguments

  • domain
    str

    A string which specifies the domain or area that the model will be specialized in.
  • classes
    dict[str, str]

    A dictionary, where each key is the name of an emotion (must be maximum 20 characters with no spaces) to classify, and each value is the description of that emotion.
  • output_path
    str
    optional

    A string which specifies the path where the output files will be generated. The output files consist of:
    • The training dataset
    • The output model safetensor and configuration files
  • num_samples
    int
    optional
    default: 500

    An integer which specifies the number of datapoints that the synthetic training dataset should consist of, and that the model will be trained on. The maximum number of datapoints you can train your model on depends on whether you are on a free or paid plan.
  • num_epochs
    str
    optional
    default: 3

    An integer which specifies the number of epochs to train the model for.
from artifex import Artifex

emotion_detection = Artifex().emotion_detection

emotion_detection.train(
    domain="customer support for an e-commerce platform",
    classes={
        "joy": "text that expresses happiness or positivity",
        "anger": "text that expresses anger or frustration",
        "sadness": "text that expresses sadness or sorrow",
        "surprise": "text that expresses surprise or shock",
        "disgust": "text that expresses disgust or revulsion",
        "excitement": "text that expresses excitement or enthusiasm",
        "neutral": "text that either expresses a neutral or indifferent emotion, or that does not express any emotion at all on account of being purely factual or descriptive."
    }
)
None